A Lazy Approach to Associative Classification

Associative classification is a promising technique to build accurate classifiers. However, in large or correlated datasets, association rule mining may yield huge rule sets. Hence, several pruning techniques have been proposed to select a small subset of high quality rules. We argue that rule pruning should be reduced to a minimum, since the availability of a "rich" rule set may improve the accuracy of the classifier. The L^3 associative classifier is built by means of a lazy pruning technique which discards exclusively rules that only misclassify training data. Classification of unlabeled data is performed in two steps. A small subset of high quality rules is first considered. When this set is not able to classify the data, a larger rule set is exploited. This second set includes rules usually discarded by previous approaches. To cope with the need of mining large rule sets and efficiently use them for classification, a compact form is proposed to represent a complete rule set in a space-efficient way and without information loss. An extensive experimental evaluation on real and synthetic datasets shows that L^3 improves the classification accuracy with respect to previous approaches.